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Found 6 papers, 5 papers with code
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture Search
However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects.
High-fidelity 3D Model Compression based on Key Spheres
Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction.
SN-Graph: a Minimalist 3D Object Representation for Classification
Using deep learning techniques to process 3D objects has achieved many successes.
Spherical Transformer: Adapting Spherical Signal to CNNs
To this end, the proposed method first uses local structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid.
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification
Voxel-based 3D object classification has been thoroughly studied in recent years.
InSphereNet: a Concise Representation and Classification Method for 3D Object
Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed method constructs a class of more representative features named infilling spheres from signed distance field (SDF).
